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地层损害是石油棘手的一个问题，而固相颗粒损害是地层损害的主要形式，因此，如何研究、量化固相颗粒的损害问题对于保护储集层防止地层损害是很重要的一项工作，本文用神经网络的方法建立了固相颗粒损害的动态模型，然后对固相颗粒损害进行了仿真研究，取得了满意的效果，为量化固相颗粒损害问题和量化是影响相颗粒损害的诸多因素（颗粒尺寸、浓度、注入速度等）的相对作用大小提供了一个快速方便的方法，应用结果表明，该方法简单

APPLICATION OF NEURAL NFTWORK TO SIMULATED MODELING OF FORMATION DAMAGE CAUSED BY SOLID PARTICLSS

In petroleum industry,formation damage is a difficult problem and its main form is solid particles damage. So how to study and quantify the damage caused by solid particles is an important task for preventing reservoir from damage. In this paper,dynamically modeling of solid particles damage is set up by neural network. Then the damage result from solids invasion is simulated,and satisfactory result is obtained,which provides an efficient and fast method for the quantification of damage due to solids or particles factors such as solid size,concentration etc. The results of application demonstrate this method is simple and general for other study.